Python IEEE Projects
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Python Final Year IEEE Projects for Computer Science, CSE, IT, MCA for 2024-2025
Novithra Tech is pioneer is developing Python Final Year IEEE Projects for CSE, IT, MCA students. Novithra Tech offers both readymade Projects and customized projects for Final Year projects for CSE. Java Projects are developed using Netbeans IDE and Database as MYSQL. Java IEEE Projects are available in all major Computer Science domains such as Cloud Computing, Data Mining, Machine Learning, NLP, Secure Computing, Network Security, Artificial Intelligence (AI), Internet of things (IoT), Web Services etc. JP INFOTECH provides Final Year Projects for CSE, Engineering, IT, B.E., B.Tech., M.E., M.Tech., M.Phil., M.S., M.Sc., MCA, B.Sc., BCA, Diploma Projects.
The following are the list of Latest Final Year Python IEEE Projects. Students can click the required project title and see the details such as the Project Base Paper Abstract, with Output of the project and Software, Hardware requirements with the Reference of the IEEE Base Paper. If the student is interested to purchase any of the project, they can just can just contact us.
| Sl. No. | Python Project Title | Domain | Algorithm / Model Used | Technology |
|---|---|---|---|---|
| 1 | Blood Cancer Identification using Hybrid Ensemble Deep Learning Technique | Deep Learning | MobileNetV2 Architecture. | Python |
| 2 | Breast Cancer Classification using CNN with Transfer Learning Models | Deep Learning | DenseNet201 Architecture. | Python |
| 3 | Calorie Estimation of Food and Beverages using Deep Learning | Deep Learning | MobileNet Architecture. | Python |
| 4 | Detection and Identification of Pills using Machine Learning Models | Deep Learning | MobileNet Architecture. | Python |
| 5 | Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods | Deep Learning | MobileNet Architecture. | Python |
| 6 | Development of Hybrid Image Caption Generation Method using Deep Learning | Deep Learning | ResNet50 Architecture + LSTM. | Python |
| 7 | Dog Breed Classification using Inception-ResNet-V2 | Deep Learning | Xception Architecture. | Python |
| 8 | Forest Fire Detection using Convolutional Neural Networks (CNN) | Deep Learning | MobileNet Architecture. | Python |
| 9 | Digital Image Forgery Detection Using Deep Learning | Deep Learning | CNN Model Architecture. | Python |
| 10 | Kidney Cancer Detection using Deep Learning Models | Deep Learning | MobileNet Architecture + Artificial Neural Network (ANN). | Python |
| 11 | Medicinal Herbs Identification | Deep Learning | Xception Architecture. | Python |
| 12 | Monkeypox Diagnosis with Interpretable Deep Learning | Deep Learning | ResNet50V2 Architecture. | Python |
| 13 | Music Genre Classification Using Convolutional Neural Network | Deep Learning | Artificial Neural Networks Model(ANN Model). | Python |
| 14 | Pancreatic Cancer Classification using Deep Learning | Deep Learning | Random Forest Classifier, Naive bayes and CNN Model Architecture. | Python |
| 15 | Prediction of Lung Cancer using Convolution Neural Networks | Deep Learning | InceptionV3 Architecture. | Python |
| 16 | Signature Fraud Detection using Deep Learning | Deep Learning | Siamese Neural Networks. | Python |
| 17 | Skin Cancer Prediction Using Deep Learning Techniques | Deep Learning | CNN Architecture. | Python |
| 18 | Traffic Sign Classification using Deep Learning | Deep Learning | MobileNet Architecture & YOLOv5. | Python |
| 19 | Disease Classification in Wheat from Images Using CNN | Deep Learning | MobileNet Architecture. | Python |
| 20 | Detection of Lungs Cancer through Computed Tomographic Images using Deep Learning | Deep Learning | CNN Model Architecture, Resnet, Naive Bayes, and K-nearest Neighbors | Python |
| 21 | A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders | Machine Learning | Random Forest Classifier & Decision Tree Classifier. | Python |
| 22 | A Machine Learning Model to Predict a Diagnosis of Brain Stroke | Machine Learning | Random Forest Classifier & Bagging Classifier. | Python |
| 23 | CO2 Emission Rating by Vehicles Using Data Science | Machine Learning | Random Forest Classifier & Decision Tree Classifier. | Python |
| 24 | Cyber Hacking Breaches Prediction and Detection Using Machine Learning | Machine Learning | Random Forest Classifier. | Python |
| 25 | Fake Profile Detection on Social Networking Websites using Machine Learning | Machine Learning | Random Forest Classifier & Decision Tree Classifier. | Python |
| 26 | Crime Prediction Using Machine Learning and Deep Learning | Machine Learning | Decision Tree Classifier and Bagging Classifier. | Python |
| 27 | Drug Recommendation System in Medical Emergencies using Machine Learning | Machine Learning | Random Forest Classifier and Decision Tree Classifier. | Python |
| 28 | Efficient Machine Learning Algorithm for Future Gold Price Prediction | Machine Learning | Random Forest Regressor. | Python |
| 29 | Heart Disease Prediction With Machine Learning | Machine Learning | Random Forest Classifier, Bagging Classifier, XG Boost & LightGBM. | Python |
| 30 | House Price Prediction using Machine Learning Algorithm | Machine Learning | Random Forest Regressor. | Python |
| 31 | Human Stress Detection Based on Sleeping Habits Using Machine Learning Algorithms | Machine Learning | Random Forest Classifier. | Python |
| 32 | Helmet and Number Plate Detection using Deep Learning | Deep Learning | YOLOv8 Architecture. | Python |
| 33 | Malware Analysis and Detection Using Machine Learning Algorithm | Machine Learning | Extra Tree Classifier and Logistic Regression. | Python |
| 34 | Efficient Detection of Diabetic Retinopathy through Deep Learning | Deep Learning | MobileNetV2 Architecture and DenseNet201 Architecture. | Python |
| 35 | Brain Stroke Detection System based on CT images using Deep Learning | Deep Learning | CNN Model Architecture and LSTM Architecture. | Python |
| 36 | Oral Cancer Detection using Deep Learning | Deep Learning | ResNet152V2 Architecture and MobileNet Architecture. | Python |
| 37 | SMS Spam Detection using Machine Learning | Machine Learning | SVC (Support Vector Classifier) and CatBoost. | Python |
| 7 | Road Pothole Detection using Deep Learning | Deep Learning | YOLOv8 Architecture. | Python |
| 8 | Intelligent Weapon Detection System for Real Time Surveillance using Deep Learning with YOLOv8 | Deep Learning | YOLOv8 Architecture. | Python |
| 9 | Yoga Pose Detection using Deep Learning | Deep Learning | YOLOv8 Architecture. | Python |
| 10 | Machine Learning based Employee Attrition Prediction and Layoff Prediction System | Machine Learning | Random Forest Classifier, Bagging Classifier & Gradient Boosting Regressor, Random Forest Regressor | Python |
| 11 | Sleep Disorder Prediction Using Machine Learning | Machine Learning | Gradient Boosting Classifier and Quadratic Discriminant Analysis. | Python |
| 12 | E-Commerce Fraud Detection Based on Machine Learning | Machine Learning | Stacking Classifier, XGB Classifier. | Python |
| 13 | Electric Vehicle (EV) Price Prediction using Machine Learning | Machine Learning | Gradient Boosting Regressor, Extratree Regressor | Python |
| 14 | Smartphone Addiction Prediction Using Machine Learning | Machine Learning | Stacking Classifier Model, CatBoost Classifier, ExtraTrees Classifier | Python |
| 15 | Obesity Risk Prediction using Machine Learning | Machine Learning | XGBoost Classifier, Stacking Classifier | Python |
| 16 | Health Insurance Price Prediction using Machine Learning | Machine Learning | Random Forest Regressor, Stacking Regressor. | Python |
| 17 | Smart Diabetes Prediction System Using Machine Learning Algorithms | Machine Learning | Stacking Classifier, ExtraTree Classifier, LGBM Classifier, CatBoost Classifier. | Python |
| 18 | A System for Automated Vehicle Damage Localization and Severity Estimation Using Deep Learning | Deep Learning | YOLOv8 Architecture. | Python |
| 19 | Deep Learning based Blood Group Detection using Fingerprint | Deep Learning | MobileNetV2 Architecture. | Python |
| 20 | Enhanced Breast Cancer Diagnosis Using Machine Learning on Patient Data and Deep Learning | Machine Learning / Deep Learning | Stacking Classifier, CatBoost Classifier & DenseNet201 Architecture. | Python |
| 21 | Deep Learning-Based Dual-Modal Bird Species Identification Using Audio and Images | Deep Learning | Artificial Neural Networks (ANN) Model, Xception Architecture. | Python |
| 22 | Plant Disease Detection using Deep Learning and Fertilizer Recommendation | Deep Learning | InceptionV3 Architecture, MobileNetV2 Architecture. | Python |
| 23 | Automated Knee Osteoarthritis Prediction and Classification from X-ray Images Using Deep Learning | Deep Learning | VGG16 Model, MobileNetV2 Model | Python |
| 24 | Cervical Cancer Prediction and Classification Using Deep Learning on Medical Image Data | Deep Learning | Xception Architecture. | Python |
| 25 | Fruit Quality Detection using Deep Learning for Rotten and Fresh Fruits Classification | Deep Learning | InceptionResNetV2 Architecture, MobileNetV2 Architecture. | Python |
Python IEEE Machine Learning Projects
🎁Project Deliverables
1) Complete Source Code with Dataset.
2) Final Report / Document (The Document which we provide is PLAGIARIZED DOCUMENT ONLY, which means that it’s not a Unique content. The Documents are with BASIC CONTENTS ONLY, COPIED/TAKEN FROM IEEE PAPER / INTERNET SOURCES. You need to customize it according to your college requirements, we don’t provide customized document)
(Document consists of basic contents of about Abstract, Bibliography, Conclusion, Implementation, I/P & O/P Design, Introduction, Literature Survey, Organisation Profile, Screen Shots, Software Environment, System Analysis, System Design, System Specification, System Study, System Testing)
(The chapter System Design consists of 5 diagrams: Data Flow, Use Case, Sequence, Class, and Activity Diagram)
3) One Review PPT.
4) Software Links required for Installation with How to Install Video.
5) “How to run” execution help file.
6) IEEE Base Paper (PDF).
Unlocking the Power of Python IEEE Projects
Python is a powerful and versatile programming language that has become increasingly popular in recent years. It’s widely used in a variety of fields, from data science and machine learning to web development and computer vision. As a computer science student, you may be wondering how to leverage Python’s capabilities in your final year project.
The good news is that Python is an excellent choice for IEEE projects. It offers a wide range of libraries and frameworks that make it easy to build complex and sophisticated software applications. From NumPy and SciPy for scientific computing to Django and Flask for web development, there’s something for every project. Additionally, Python’s simplicity and readability make it a great choice for beginners and experienced developers alike.
Additionally, Python is widely used in research and industry, and it’s supported by many leading organizations and companies. JP INFOTECH provides readymade Python IEEE Projects in Machine Learning, IEEE Projects in Deep Learning, IEEE Projects in Data Science and IEEE Projects in Artificial Intelligence.
| Sl. No. | Python Machine Learning Project Title | Domain | Algorithm / Model Used | Technology |
|---|---|---|---|---|
| 1 | Detection of Phishing Websites Using Machine Learning | Machine Learning | Gradient Boosting Classifier. | Python |
| 2 | Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis | Machine Learning | Random Forest Regressor | Python |
| 3 | Electricity Theft Detection in Smart Grids Based on Deep Neural Network | Machine Learning | Artificial Neural Network (ANN). | Python |
| 4 | Intrusion Detection System Using Improved Convolution Neural Network | Machine Learning | Decision Tree Classifier. | Python |
| 5 | Intrusion Detection System Using PCA with Random Forest Approach | Machine Learning | Random Forest Classification | Python |
| 6 | A Machine Learning-based Approach for Crop Yield Prediction and Fertilizer Recommendation | Machine Learning | Extra Trees Regressor, Gaussian NB. | Python |
| 7 | InteliCrop: An Ensemble Model to Predict Crop using Machine Learning Algorithms | Machine Learning | Ensemble Model (VotingClassifier). Used three algorithm 1) Random Forest Classifier, 2) Logistic Regression, 3) Gradient Boosting Classifier. | Python |
| 8 | Agricultural Crop Recommendations based on Productivity and Season | Machine Learning | Decision Tree Classifier | Python |
| 9 | Crop Recommender System Using Machine Learning Approach | Machine Learning | Random Forest | Python |
| 10 | Crop Yield Prediction based on Indian Agriculture using Machine Learning | Machine Learning | Stacked Regression(Lasso, Kernel Ridge,ENet) | Python |
| 11 | Edible and Poisonous Mushrooms Classification by Machine Learning Algorithms | Machine Learning | Decision Tree Classifier. | Python |
| 12 | An Efficient Spam Detection Technique for IoT Devices Using Machine Learning | Machine Learning | Random Forest Classification | Python |
| 13 | Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms | Machine Learning | Gradient Boosting Classifier. | Python |
| 14 | Fraud Detection and Analysis for Insurance Claim using Machine Learning | Machine Learning | Random Forest Classifier. | Python |
| 15 | Fraud Detection on Bank Payments Using Machine Learning | Machine Learning | Random Forest Classifier. | Python |
| 16 | A Comparative Study on Fake Job Post Prediction Using Different Data mining Techniques | Machine Learning | Random Forest Classifier, K-Neighbors Classifier | Python |
| 17 | WELFake: Word Embedding Over Linguistic Features for Fake News Detection | Machine Learning | CNN Model Architecture | Python |
| 18 | Detecting Fake Reviews Using Multidimensional Representations With Fine-Grained Aspects Plan | Machine Learning | CNN Model Architecture | Python |
| 19 | Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms | Machine Learning | Passive-Aggressive | Python |
| 20 | Email Spam Detection Using Machine Learning Algorithms | Machine Learning | Passive-Aggressive | Python |
| 21 | Defensive Modeling of Fake News Through Online Social Networks | Machine Learning | Passive-Aggressive | Python |
| 22 | FAKEDETECTOR: Effective Fake News Detection with Deep Diffusive Neural Network | Machine Learning | Passive-Aggressive | Python |
| 23 | Spam Review Detection Using the Linguistic and Spammer Behavioral Methods | Machine Learning | Logistic Classification | Python |
| 24 | Review Spam Detection using Machine Learning | Machine Learning | Naive Bayes Classifier. | Python |
| 25 | A Deep Prediction of Chronic Kidney Disease by Employing Machine Learning Method | Machine Learning | Artificial Neural Network (ANN). | Python |
| 26 | Efficient Thyroid Disease Prediction using Features Selection and Meta-Classifiers | Machine Learning | Decision Tree Classifier. | Python |
| 27 | Heart Disease Prediction using Machine Learning and Data Analytics Approach | Machine Learning | Artificial Neural Network (ANN). | Python |
| 28 | Liver disease prediction using Ensemble Technique | Machine Learning | Gradient Boosting Classifier + AdaBoost Classifier (Ensemble Technique) | Python |
| 29 | Prediction of Parkinson’s disease using XGBoost | Machine Learning | Random Forest Classifier. | Python |
| 30 | Chronic Kidney Disease Stage Identification in HIV Infected Patients using Machine Learning | Machine Learning | Support Vector Machine (SVM) | Python |
| 31 | Diabetes Disease Prediction Using Machine Learning Algorithms | Machine Learning | Random Forest Classifier | Python |
| 32 | Machine Learning Based Heart Disease Prediction System | Machine Learning | Decision Tree Classifier | Python |
| 33 | Naive Bayes Classifier for Predicting the Novel Coronavirus | Machine Learning | Random Forest Classifier | Python |
| 34 | Primary Stage of Diabetes Prediction using Machine Learning Approaches | Machine Learning | Random Forest Classifier | Python |
| 35 | A Machine Learning Methodology for Diagnosing Chronic Kidney Disease | Machine Learning | Logistics regression | Python |
| 36 | Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning | Machine Learning | Linear SVC | Python |
| 37 | COVID-19 Future Forecasting Using Supervised Machine Learning Models | Machine Learning | Random Forest Regressor | Python |
| 38 | Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare | Machine Learning | Logistics regression | Python |
| 39 | Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis | Machine Learning | Logistics regression | Python |
| 40 | A New Intelligent Approach for Effective Recognition of Diabetes in the IoT E-HealthCare Environment | Machine Learning | Ensemble Decision Tree | Python |
| 41 | Detection of Cyberbullying Using Machine Learning and Deep Learning Algorithms | Machine Learning | LSTM architecture. | Python |
| 42 | Toxic Speech Classification using Machine Learning Algorithms | Machine Learning | Random Forest Classifier. | Python |
| 43 | Detection of Cyberbullying on Social Media Using Machine learning | Machine Learning | Support Vector Machine, Random Forest Classifier | Python |
| 44 | Detecting A Twitter Cyberbullying Using Machine Learning | Machine Learning | Naïve Bayes | Python |
| 45 | Deep Learning Based Fusion Approach for Hate Speech Detection | Machine Learning | CNN Model Architecture | Python |
| 46 | A Machine Learning Based Approach for Wine Quality Prediction | Machine Learning | Random Forest Classifier. | Python |
| 47 | Air Quality Prediction Based on Machine Learning | Machine Learning | Random Forest Classifier. | Python |
| 48 | Water Quality Classification Using SVM And XGBoost Method | Machine Learning | Gradient Boosting Classifier. | Python |
| 49 | Traffic Accident Risk Prediction Using Machine Learning | Machine Learning | Random Forest Classifier. | Python |
| 50 | A Road Accident Prediction Model Using Data Mining Techniques | Machine Learning | Adaboost Classifier Algorithm | Python |
| 51 | Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning | Machine Learning | Decision Tree Regressor. | Python |
| 52 | Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms | Machine Learning | Decision Tree Regression | Python |
| 53 | Prediction of Modernized Loan Approval System Based on Machine Learning Approach | Machine Learning | Support Vector Machine (SVM) | Python |
| 54 | A Study on a Car Insurance Purchase Prediction Using Machine Learning | Machine Learning | Random Forest Classifier | Python |
| 55 | Flight Delay Prediction Based on Aviation Big Data and Machine Learning | Machine Learning | Random Forest Classification | Python |
| 56 | Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data | Machine Learning | Random Forest Classification | Python |
| 57 | Performance Analysis on Student Feedback using Machine Learning Algorithms | Machine Learning | Naïve Bayes | Python |
| 58 | Students Performance Prediction in Online Courses Using Machine Learning Algorithms | Machine Learning | Random Forest Classification | Python |
| 59 | A Systematic Review of Predicting Elections Based on Social Media Data | Machine Learning | Passive Aggressive Classifier | Python |
| 60 | COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis | Machine Learning | Support Vector Machine (SVM) | Python |
| 61 | Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD) | Machine Learning | Voting Classifier (LOGISTIC REGRESSION AND STOCHASTIC GRADIENT DESCENT) | Python |
| 62 | Detection of Fake and Clone accounts in Twitter using Classification and Distance Measure Algorithms | Machine Learning | Random Forest Classification | Python |
| 63 | Detection of Malicious Social Bots Using Learning Automata With URL Features in Twitter Network | Machine Learning | Logistics regression | Python |
| 64 | Finding Psychological Instability Using Machine Learning | Machine Learning | Random Forest Classification | Python |
| 65 | Hybrid Feature based Prediction of Suicide Related Activity on Twitter | Machine Learning | Naïve Bayes | Python |
| 66 | Analysis and Prediction of Suicide Attempts | Machine Learning | Logistic Regression | Python |
| 67 | A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining | Machine Learning | Mini batch gradient descent algorithm | Python |
| 68 | Crime Type and Occurrence Prediction Using Machine Learning Algorithm | Machine Learning | Random Forest Classifier | Python |
| 69 | Comparison of Machine Learning Algorithms for Predicting Crime Hotspots | Machine Learning | Random Forest Classification | Python |
| 70 | Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews | Machine Learning | Naïve Bayes | Python |
Python IEEE Deep Learning Projects
| Sl. No. | Python Deep Learning Project Title | Domain | Algorithm / Model Used | Technology |
|---|---|---|---|---|
| 1 | A Contemporary Technique for Lung Disease Prediction using Deep Learning | Deep Learning | VGG16 Architecture. | Python |
| 2 | Parkinson’s Disease Detection Using Machine Learning | Deep Learning | Xception Architecture. | Python |
| 3 | Pneumonia Detection using Deep Learning | Deep Learning | VGG16 Architecture. | Python |
| 4 | Brain Tumour Detection Using Deep Learning | Deep Learning | CNN Model Architecture | Python |
| 5 | Classification of Malaria-Infected Cells using Convolutional Neural Networks | Deep Learning | CNN Model Architecture | Python |
| 6 | Diabetic Retinopathy Detection by means of Deep Learning | Deep Learning | InceptionV3 Architecture | Python |
| 7 | Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network | Deep Learning | EfficientNet B0 Architecture | Python |
| 8 | Melanoma Detection Using Convolutional Neural Network | Deep Learning | VGG16 architecture | Python |
| 9 | Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment | Deep Learning | CNN Model Architecture | Python |
| 10 | Malaria Detection using Deep Learning | Deep Learning | Inception V3 Architecture | Python |
| 11 | Automated Bird Species Identification using Audio Signal Processing and Neural Network | Deep Learning | Artificial Neural Networks model (ANN model) | Python |
| 12 | Speech Emotion Recognition using Machine Learning | Deep Learning | Artificial Neural Network model (ANN model) | Python |
| 13 | Detection of Stress in IT Employees using Machine Learning Technique | Deep Learning | CNN Model Architecture. | Python |
| 14 | A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection | Deep Learning | CNN Model Architecture | Python |
| 15 | Real-Time Drowsiness Identification based on Eye State Analysis | Deep Learning | OpenCV | Python |
| 16 | A Mask Detection Method for Shoppers Under the Threat of COVID-19 Coronavirus | Deep Learning | CNN Model Architecture | Python |
| 17 | Human Recognition using Ear based Deep Learning Features | Deep Learning | CNN Model Architecture | Python |
| 18 | Fine-Grained Food Classification Methods on the UEC FOOD-100 Database | Deep Learning | MobileNet Architecture. | Python |
| 19 | Identification of Fake Indian Currency using Convolutional Neural Network | Deep Learning | Xception Architecture. | Python |
| 20 | Plant Disease Detection and Classification Using Machine Learning Algorithm | Deep Learning | InceptionV3 Architecture. | Python |
| 21 | Rice Leaf Disease Prediction Using Machine Learning | Deep Learning | MobileNetV2 Architecture. | Python |
| 22 | Soil Analysis and Crop Recommendation using Machine Learning | Deep Learning | MobileNetV2 Architecture. | Python |
| 23 | Potato Disease Detection Using Machine Learning | Deep Learning | VGG16 Architecture. | Python |
| 24 | Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network | Deep Learning | VGG16 Architecture. | Python |
| 25 | Rice Leaf Diseases Classification Using CNN With Transfer Learning | Deep Learning | Inception V3 Architecture. | Python |
| 26 | Traffic Sign Board Recognition and Voice Alert System using Convolutional Neural Network | Deep Learning | CNN Model Architecture | Python |
| 27 | Deep Learning for Large-Scale Traffic-Sign Detection and Recognition | Deep Learning | CNN Model Architecture | Python |
| 28 | License Plate Detection Methods Based on OpenCV | Deep Learning | OpenCV | Python |
| 29 | Handwritten Digit Recognition Using CNN | Deep Learning | CNN Model Architecture | Python |
🙌 Why Choose Novithra Tech?
Because we offer everything you need in one place — and at a price that won’t break your budget!
✅ Complete Project Package
Get full source code, research paper (PDF), PPT, blackbook, and documentation – all included!
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Whether it’s an IEEE project or a final year app-based project, we’ve got you covered.
💰 Affordable & Student-Friendly
High-quality projects at student-friendly prices – no hidden costs!
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🌟 Why Choose Python IEEE Projects?
IEEE is a globally respected authority for setting standards in technology and innovation. Here’s why Python IEEE Projects from Novithra Tech are a smart choice for your academic and research success:
📌 Looking for Final Year Projects?
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🤖 Python Machine Learning IEEE Projects – Pioneering Innovation
Welcome to Novithra Tech’s Machine Learning IEEE Projects Hub, where we blend the power of Machine Learning 🤖 with the reliability of IEEE standards.
If you’re a student or researcher aiming for cutting-edge research, you’re in the right place! 🚀
✅ Why Choose Machine Learning IEEE Projects?
🌍 Industry-Relevant: IEEE projects are aligned with the latest trends in technology and real-world applications.
🧠 High Standards: Projects follow strict IEEE guidelines ensuring quality, ethics, and reliability.
🌐 Diverse Applications: Explore exciting areas like NLP, Computer Vision, Robotics, and more!
🐍 Why Python for IEEE Projects?
Python is one of the most popular programming languages today — perfect for both beginners and pros!
👨💻 Easy to Learn – Great for students starting out.
📚 Powerful Libraries – Tools like NumPy, Pandas, TensorFlow, etc., make development a breeze.
🤖 Versatile – Ideal for AI, Data Science, IoT, and more!
🚀 How to Get Started with Novithra Tech?
🔍 Browse Projects – Explore our wide collection of Python IEEE Projects.
🎯 Choose a Topic – Pick one that matches your interest or career goal.
💻 Get the Code – Receive complete source code, documentation, and project files.
📈 Learn & Implement – Understand your project and present it with confidence!
🎓 Get Your Python IEEE Project Today!
At Novithra Tech, we help you take the leap from student to innovator.
Our IEEE-standard Python projects are designed to equip you with practical skills and industry-ready knowledge.
✨ Start your journey in research and innovation today —
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